5.4 The end-to-end data science workflow

Key points

1. Ingest means bringing raw data in. ETL, cleaning and transformation prepare it for training. ETL means extract, transform, load.

   What NVIDIA says (1)

   “RAPIDS aims to accelerate the entire data science pipeline including data loading, ETL, model training, and inference.”

   — RAPIDS Accelerates Data Science End-to-End

2. Cleaning fixes types, gaps and errors so data can be analyzed. cuDF offers the same style of API on GPUs. MLOps means machine learning operations. API means application programming interface.

   What NVIDIA says (1)

   “With its support for structured data formats like tables, matrices, and time series, the pandas Python API provides tools to process messy or raw datasets into clean, structured formats ready for analysis.”

   — What Is Pandas and Why Does it Matter?

3. Each move between tools or devices costs time. Keeping the whole pipeline on the GPU removes most of those moves. ETL means extract, transform, load.

   What NVIDIA says (1)

   “To get the highest performance for your ML pipeline on NVIDIA GPUs, minimize data transfers between the CPUs and GPUs as part of your pipeline.”

   — NVIDIA cuML Brings Zero Code Change Acceleration to scikit-learn

Key terms

• RAPIDS: NVIDIA's open-source GPU libraries for data science, covering loading, ETL, training and analysis.
• ETL: The steps that pull raw data, clean and reshape it, and store it for analysis.
• Data science pipeline: The chain of steps from loading data to training and serving a model.

Sample question

Which list matches the early stages of a data science workflow before modeling?